To demonstrate this, we use the most widely used segmentation network U-Net [1]}. U-Net is trained on the FS images to produce multi-class (LV, RV and MC) segmentation outputs. Since the ground truth segmentation masks are unavailable for the test set of the ACDC dataset, we instead use the outputs of the FS images in the test set as ground truth. The reconstructed images from GAN and Recon-GLGAN are passed to the UNet and the corresponding segmentation masks are obtained. The obtained segmentation masks for sample images are shown in Figure REF . It is evident from the figure that our network's performance is closest to FS followed by GAN and ZF images. The same are quantified using the segmentation metrics Dice and Hausdorff for the sample images in Figure REF .
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